US 6823082 B2 Abstract A method of compressing digital representations of images stores the images in multiple subsampling quality scales in a progressive manner such that a higher quality scale contains only data incremental to the data in an adjacent lower quality scale. The data in each quality scale is transformed, quantized, and entropy encoded. A discrete wavelet cosine transformation combining frequency transformation features of a discrete cosine transformation and spatial transformation features of a wavelet transformation is provided. Sequences of subsampling quality scales are provided for multi-scale representations of images. A novel context selection method is used which treats color components and coefficients of different positions differently. An image compressed in a given quality scale can be transmitted or decompressed progressively allowing progressive downloading or transmission over the Internet.
Claims(31) 1. A method of compressing a digital representation of an image into a one-dimensional bitstream, the digital representation comprising a two-dimensional array of pixels wherein a primary color component and secondary color components are associated with each pixel, the method comprising:
representing the image in a series of quality scales of progressively decreasing quality, wherein a higher quality scale comprises more data than a lower quality scale, and wherein lower quality scales are formed from higher quality scales by decreasing the number of stored color components or by decreasing the number of pixels;
representing the image in terms of quality scales in terms of a base quality scale image and differential images wherein a differential image at a given quality scale is the difference between the image at the given quality scale and a representation of the image scaled up from a reconstructed representation at the adjacent lower quality scale, the reconstructed representation determined by a process comprising;
transforming the image into a set of coefficients associated with known functions,
quantizing the set of coefficients by dividing by quantization values and rounding to integer values,
dequantizing the set of quantized coefficients by multiplying by the quantization values, and
performing the inverse transform associated with the known functions to produce a reconstructed representation;
representing the base quality scale image and the differential images as integer values by a process comprising transforming to a set of coefficients associated with known functions and quantizing the set of coefficients by dividing by quantization values and rounding to integer values; and
encoding the integer values corresponding to the lowest quality scale and the differential images by a lossless ordered statistics encoding process to produce a one-dimensional bitstream.
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15. A method of reconstructing a digital representation of an image compressed by the method of
recovering context prediction probability tables used in compression;
decoding integer values corresponding to the base quality scale representation and the differential images;
reverse ordering each decoded integer value to a two-dimensional position in an array of pixels;
multiplying each decoded integer value corresponding to the base quality scale and the differential images by the quantization value;
performing the inverse transform associated with the known functions to reconstruct the digital representation of the base quality scale and the differential images;
upscaling the digital representation of an image at a lower quality scale to the next higher quality scale; and
adding a differential image at a given quality scale to the upscaled image at the given quality scale to reconstruct the digital representation at the given quality scale.
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21. An apparatus comprising instructions for performing a method of compressing a digital representation of an image into a one-dimensional bitstream, the digital representation comprising a two-dimensional array of pixels wherein a primary color component and secondary color components are associated with each pixel, the method comprising:
representing the image in a series of quality scales of progressively decreasing quality, wherein a higher quality scale comprises more data than a lower quality scale, and wherein lower quality scales are formed from higher quality scales by decreasing the number of stored color components or by decreasing the number of pixels;
representing the image in terms of quality scales in terms of a base quality scale image and differential images wherein a differential image at a given quality scale is the difference between the image at the given quality scale and a representation of the image scaled up from a reconstructed representation at the adjacent lower quality scale, the reconstructed representation determined by a process comprising;
transforming the image into a set of coefficients associated with known functions,
quantizing the set of coefficients by dividing by quantization values and rounding to integer values,
dequantizing the set of quantized coefficients by multiplying by the quantization values, and
performing the inverse transform associated with the known functions to produce a reconstructed representation;
representing the base quality scale image and the differential images as integer values by a process comprising transforming to a set of coefficients associated with known functions and quantizing the set of coefficients by dividing by quantization values and rounding to integer values; and
encoding the integer values corresponding to the lowest quality scale representation and the differential images by a lossless ordered statistics encoding process to produce a one-dimensional bitstream.
22. The apparatus of
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26. An apparatus comprising instructions for reconstructing a digital representation of an image compressed by the method of
recovering context prediction probability tables used in compression;
decoding integer values corresponding to the base quality scale representation and the differential images;
reverse ordering each decoded integer value to a two-dimensional position in an array of pixels;
multiplying each decoded integer value corresponding to the base quality scale and the differential images by the quantization value;
performing the inverse transform associated with the known functions to reconstruct the digital representation of the base quality scale and the differential images;
upscaling the digital representation of an image at a lower quality scale to the next higher quality scale; and
adding a differential image at a given quality scale to the upscaled image at the given quality scale to reconstruct the digital representation at the given quality scale.
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Description This application is a continuation-in-part of U.S. application Ser. No. 09/792,668, now U.S. Pat. No. 6,757,429 filed Feb. 21, 2001. This invention relates generally to compression of digital images and in particular to methods of digital image compression that store or transmit compressed image data in multiple quality scales in a progressive manner. Digital storage and display of high quality color images has become ubiquitous. In order to overcome massive storage requirements and reduce transmission time and cost of high quality digital images, data compression methods have been developed. In particular, the method known as JPEG and the recent update known as JPEG2000 have become industry standards. Data compression generally involves a tradeoff between data size and reconstructed image quality. When reconstructed images differ from the original image, the data compression method is said to be “lossy.” As is well known, in the basic JPEG method, an image is transformed into a luminance/chrominance color representation conventionally denoted as YUV or YCbCr, where Y is a primary color or luminance component and U and V or Cb and Cr are secondary color components. The number of secondary components stored is reduced by averaging together groups of pixels. The pixel values for each component are grouped into blocks and each block is transformed by a discrete cosine transform (DCT). In each block, the resulting DCT coefficients are quantized, that is divided by a predetermined quantization coefficient and rounded to integers. The quantized coefficients are encoded based on conditional probability by Huffman or arithmetic coding algorithms known in the art. A normal interchange JPEG file includes the compression parameters, including the quantization tables and encoding tables, in the file headers so a decompressor program can reverse the process. Optional extensions to the minimum JPEG method include a progressive mode intended to support real time transmission of images. In the progressive mode, the DCT coefficients may be sent piecemeal in multiple scans of the image. With each scan, a decoder can produce a higher quality rendition of the image. However, in most implementations, the same number of pixels is used at each level of quality. Despite the widespread implementation of the JPEG and JPEG2000 methods, each method has its own drawbacks. The major problems in JPEG compression include a moderate compression ratio, a block effect, and poor progressive image quality. A major step used in JPEG to achieve reasonable data compression is to quantize the DCT coefficients. However, light quantization leads to a low compression ratio while heavy quantization leads to block effects in which block boundaries can be seen in reconstructed images. Using the JPEG method, image quality does not degrade gracefully with compression ratio. Therefore, a progressively decoded JPEG image is not pleasing to the viewer until the last scan of the image is decoded. JPEG2000 is designed to overcome some of the drawbacks of JPEG. JPEG2000 uses a wavelet transform that degrades more gracefully as the compression ratio increases. However, JPEG2000 comes with a price of increased computational complexity. The progression methods employed in JPEG2000 require excessive computational power for both encoding and decoding. While the wavelet transform in JPEG2000 improves quality degradation with respect to compression ratio, it does not improve data compaction intrinsically, such that the compression ratio is about the same as that of JPEG when high quality is required. Further, the context prediction method used for arithmetic coding in JPEG2000 does not take advantage of the fact the colors of objects in a picture are highly correlated. Therefore, there remain opportunities to improve existing technologies for image compression. It would be desirable to provide a better transform that has fast implementations and makes data more compact. A more efficient and better quality progression method is also desired. Finally, there is an opportunity to utilize color correlation in context prediction and to provide a compression method for color spaces other than the YUV space. A method of compressing digital representations of images provides the ability to store the images in multiple subsampling quality scales in a progressive manner such that a higher quality scale contains only data incremental to the data in an adjacent lower quality scale. The method can be implemented in software, in dedicated hardware, or in a combination of software and hardware. The method is primarily applied to three-color images represented in terms of a primary color component and secondary color components, associated with pixels forming a two-dimensional array. Multiple color spaces, for example, the RGB space or the YUV luminance/chrominance color space can be treated. According to the method, first an image is represented in a sequence of quality scales of progressively decreasing quality. In the sequence, a lower quality scale is formed from a higher quality scale by decreasing the number of stored color components or by decreasing the number of pixels of some or all of the color components. In one useful scale sequence, for the first, that is the highest, quality scale, all color components are present for each pixel. At the second quality scale, the primary color component and one secondary color component are present for each pixel. At the third quality scale, a primary color component is present at each pixel and twice as many primary color components as secondary color components are present. The sequence also includes fourth, fifth, and sixth quality scales derived from the first, second, and third quality scales, respectively, by reducing the number of pixels by a downscaling process. Downscaling processes such as decimation scaling, bilinear scaling, or bicubic scaling may be used. A second useful scale sequence of quality scales includes the first, second, and third scales described above together with a fourth quality scale in which one color component is present at each pixel location and twice as many primary components as secondary components are present. The latter scale is known as the Bayer pattern. Each representation at a higher quality scale is represented in terms of a differential with respect to the image at the adjacent lower quality scale. Each differential image contains only data incremental to the corresponding lower quality scale. The differential images are determined from reconstructed images at the adjacent lower quality scale which avoids accumulation of error. The original representation is thus transformed into the representation at the lowest quality scale plus the differential images. As part of the process of representing the image as differentials, the base quality scale image and the differential images are transformed into a set of coefficients associated with known functions. In typical implementations, the lowest quality scale representation and the differential images are each divided into blocks before the transform stage. In conventional JPEG methods, a discrete cosine transformation is used. According to an aspect of the present invention, a transformation termed the discrete wavelet cosine transformation (DWCT) which combines the frequency transformation features of a discrete cosine transformation and the spatial transformation, multi-resolution features of the Haar wavelet transformation may be used. The DWCT is defined recursively from a discrete cosine transform and a permutation function whereby output elements of the transform are separated into a first portion and a second portion, the first portion containing lower scales of representation of input to the transform. The DWCT transformation is both faster than conventional wavelet transformations and provides a better compaction of coefficient values than previously used transformations. The DWCT coefficients are quantized by dividing by values specified in quantization tables and rounding to integers. Quantized coefficients corresponding to the base quality scale and the differential images are compressed by a lossless ordered statistics encoding process. The ordered statistics encoding process includes the stages of context prediction, ordering the two-dimensional array into a one-dimensional array, and arithmetic encoding. According to another aspect of the invention, the process of context prediction, that is predicting the value of each coefficient from the values of coefficients at neighboring pixels, predicts each color component separately. For the primary color component, the context for a given pixel comprises a positional index and neighboring coefficients of primary color pixels. For a first secondary color, the context comprises a positional index, coefficients of neighboring first secondary color components, and the coefficient of the corresponding primary color component of the same positional index. For a second secondary color component, the context comprises a positional index, neighboring second secondary color coefficients, and the coefficients of the corresponding primary and first secondary color components of the same positional index. In the present context prediction method, the coefficients are divided into four groups based on position in the array and the position of neighboring coefficients used for context prediction differs for each group. According to yet another aspect of the present invention, an ordering process defined here as the quad-tree ordering method is used to maximize data correlation. In the quad-tree ordering method, the two-dimensional array of coefficients is partitioned into four equally sized regions ordered as upper left, upper right, lower left, and lower right. Each region is repeatedly partitioned into four equally sized subregions ordered as upper left, upper right, lower left, and lower right until a subregion of one pixel by one pixel in size is obtained. Ordering can be done before quantization or context prediction as long as the mapping is preserved for all relevant data such as coefficients, quantization tables, and contexts. The context-predicted, ordered coefficient values are then encoded using a lossless encoding method, for example an arithmetic encoding method. The present compression process produces a bitstream that can be efficiently stored or transmitted over a computer network. A decompression process essentially reverses the process and thus enables the image to be reconstructed. An image compressed according to the present process can be progressively viewed or downloaded by transmission over a computer network or the Internet. Further, a browser can display an image at a specified quality scale, ignoring any data corresponding to quality scales higher than the specified scale. FIG. 1 is a flow diagram of a process of compressing digital still images according to embodiments of the present invention. FIG. 2 is a flow diagram of a process of decompressing bitstream data into digital images according to embodiments of the present invention. FIG. 3 illustrates groups 0 to 3 used for context prediction according to an embodiment of the present invention. Methods of compressing digital representations of images according to embodiments of the present invention represent the images in multiple subsampling quality scales in a progressive manner that avoids accumulation of error. In this application, the methods are discussed with reference to still images. However, since each image in any kind of video sequence can be treated as a still image, the present methods are also applicable to video images. The images treated by the present methods are primarily three-color images represented in terms of a primary color component (denoted by P) and secondary color components (denoted by S and Q,) associated with pixels forming a two-dimensional array. However, the methods can be extended to single-color or multi-color images, as well. The P, S, and Q components correspond, for example, to the G, R, and B components, respectively, in the RGB color space and to the Y, U, and V components, respectively in the YUV luminance/chrominance color space. Typically, then, the input to the data compression methods is a two-dimensional array of pixels each having three color components, and the output is a one-dimensional bitstream of compressed data. The methods can be implemented in software running on a general purpose computer processing unit (CPU) or a digital signal processor (DSP), in hardware, for example a VLSI chip, or in a combination of hardware and software. When the methods are implemented in software, the computer instructions for carrying out the methods may be stored in a memory associated with the CPU or DSP. Thus, the term apparatus, as used here, refers to a dedicated hardware apparatus with pre-programmed instructions, general purpose computer and associated memory with stored instructions, or any combination of dedicated hardware and computers executing instructions. A compressed image may be stored on a memory for later retrieval and display on a monitor or may be transmitted over an internal network or an external network such as the Internet. An overview of the present methods of compressing digital representations of images is given in the flow diagram of FIG. The differentiation stage, The quantized base image and differential images are converted to a bitstream of compressed data by a lossless ordered statistics encoding process. The ordered statistics encoding process includes a context prediction stage In one embodiment, a total of seven subsampling scales are defined for use at subsampling stage In scale 0, denoted 4:4:4, the original image is not subsampled at all. All P, S, and Q components are kept at all pixel positions, as shown in Table 1.
In scale 1, denoted 4:2:2, the primary component P is not subsampled at all; only S and Q components are subsampled. There are six modes of subsampling in this scale, as shown in Tables 2-7 below.
Mode 1 of the 4:2:2 subsampling method is the same as the subsampling scheme used in the TV industry and as the MPEG2 Standard for transmitting moving images. In the tables, an “x” indicates a missing component. In the second mode, shown in Table 3, the S and Q components are diagonally aligned so that the spacing is uniform along horizontal and vertical directions for S and Q components. Mode 2 is a preferred subsampling mode for scale 1.
The third mode is a trivial variation of Mode 2 with the positions of S and Q interchanged, as shown in Table 4.
The fourth mode is a transposed mode of Mode 1, with spacing favoring the horizontal direction.
The fifth and sixth modes are trivial variations of Mode 1 and 4, with the positions of S and Q interchanged.
In scale 2, denoted 4:2:0, the primary component P is also not subsampled at all; only S and Q components are subsampled. There are many modes of subsampling in this scale, by locating S and Q in different positions. The five most useful modes are the following.
The first mode is similar to the YUV 4:2:0 chroma format used in MPEG2 and JPEG standards. In this mode, every four pixels of P share a pair of S and Q components, located at the center of the four P pixels. In the second mode, the S and Q components are not co-sited. Rather, they are aligned diagonally and co-sited with a different P pixel, as shown in Table 9.
The other three modes are just variations of Mode 2, with S and Q distributed over different locations, as shown in Tables 10-12.
Scale 3 is the Bayer Pattern which is often used as a sampling pattern in imaging sensor technology. In scale 3 there are four modes, each defining a particular structure of color components. In the first mode, shown in Table 13, only one color component is preserved as each pixel location. The primary color has twice as many elements as the secondary colors.
The other modes of Bayer Pattern Subsampling are simple rearrangements of the component positions as illustrated in Tables 14-16 below.
In scale 4, the number of pixels is decreased. If the size of each pixel in a display remains the same, the overall size of the displayed image will be smaller at scale 4. If the overall image size of the display is not reduced, then each pixel at scale 4 represents a larger area of the image. The color components are represented in the 4:4:4 pattern of scale 0. In scale 4, an original image I
Then, I Many algorithms can be used to scale down I In scale 5, an image I The scaling-down and scaling-up algorithms are used in pairs, denoted by (Lx↓,Lx↑), where L is a scaling factor. and Lx defines the scaling algorithm. The optimal choice of particular scaling algorithm depends on the characteristics of the image data. By pairing, the same L is used for both scaling down (Lx↓) and scaling up (Lx↑). Scaling may be performed on some or all of the color components of an image, depending on the subsampling patterns and from which scale an image is subsampled. To form a sequence of subsampled images, first, an original image I must be in one of the representations of Scales 0, 1, 2, and 3 described above. An image in the 4:2:2 pattern is obtained from an image in the 4:4:4 pattern with scaling down the UV or RB components by a factor of 2 along only the horizontal direction. This scaling down along the horizontal direction is performed by a 2x↓ algorithm. The vertical sizes of the YUV or RGB components are the same for a 4:4:4 pattern and a 4:2:2 pattern. Similarly, an image in the 4:2:0 pattern is obtained from an image in the 4:2:2 pattern by scaling down the UV or RB components by a factor of 2 along only the vertical direction. A Bayer pattern is obtained from a 4:2:0 image with scaling down the P component image horizontally or vertically, but not both. Let G be the original scale of image I. Then I is represented by I For example, assume G=2, and S=4. Then I The color components in the Bayer Pattern are not co-sited. When subsampling a Bayer Pattern into a format of lower scale, care needs to be taken on pixel alignment. Therefore, the following two sequences of scales are particularly useful: Sequence I: {0, 1, 2, 4, 5, 6} Sequence II: {0, 1, 2, 3} For SQS values less than 3, either Sequence I or Sequence II can be used. For an SQS value of 3, Sequence II is used. For SQS values greater than 3, Sequence I is used. In Sequence II, the decimation method is preferred in the subsampling at Scales 1 and 2. In Sequence I, Scale 3 is skipped in order to avoid the non-cositedness problem. More sophisticated scaling methods can be used for Sequence I. A sequence of differential images may be defined differently for Sequences I and II. In the case of Sequence II, the subsampled sequence contains a subset of {I
For convenience, let D {D In the case of Sequence I, the subsampled sequence contains a subset of {I
Let D {D The differentiation method in Sequence I can also be applied Sequence II. According to an aspect of the present invention, instead of the sequence of differentially subsampled images described above, the differential images produced at the differentiation stage We consider here only the case of Sequence I. The case of Sequence II is simpler and can be similarly treated. Given an SQS S and a CQS C, first obtain the subsampled sequence {I Reconstructing I
and D Reconstructing D'
where the symbol F
Now D The calculations described immediately above are illustrated in the flow diagram of FIG. As indicated, the transform stage In the following formulas, a bold face letter such as X or Y is used to represent a vector. A subscript is used to denote the dimension of the vector. For example, X If X
Given a vector X
T
for some matrix T The DWCT is defined recursively. To clarify the relationship between the DWCT and the previously used DCT and wavelet transforms, first a recursive definition of the DCT is provided. For a 2-element vector X Then the DCT of an N-element vector X
where C
and
the DCT at size N can be recursively defined in terms of DCTs at size N/2 through
with
Note that Q The DCT kernel C The Haar Transform (HT) may also be defined recursively. For a 2-element vector X Then HT of an N-element vector X
where H E and
the HT at size N can be recursively defined in terms of HTs at size N/2 through
with
It is clear that E As in the recursive definition of the DCT, the HT kernel H The one-dimensional DWCT according to an aspect of the present invention is also defined recursively. First, the DWCT W Next, the DWCT W
Again, two vectors E and We still call E
with Again, we assume Q As in the recursive definitions of the DCT and the HT, the DWCT kernel W Although W The discrete wavelet cosine transform takes its name from Eqs. (6)-(9). The even part, E While the DWCT is defined here in the context of the present data compression process in which differential images are defined from reconstructed representations, the DWCT may be applied in any data compression method. For example, alternate data compression methods could be defined in which DWCT replaces the DCT or wavelet transforms of JPEG or JPEG2000, respectively. Obviously, the inverse of W
for some reversible matrix W The manner in which the recursive definition of the DWCT is applied to obtain the transform W The two-dimensional DWCT of an array X
First the transform is applied to the rows of the two-dimensional array, treating each row as a one-dimensional vector, and then the transform is applied to the columns of the array, again treating each column as a one-dimensional vector. The DWCT is applied to each color component separately. That is, each color component is extracted as a monochromatic component image and then the DWCT is applied to each of the component images. In the case that the length of an image is not an even power of 2, according to another aspect of the present invention, a low-frequency extension method is applied. First consider the one-dimensional case. A vector X Consider the inverse DWCT of X By setting Z
X In order to reduce the computation of the DWCT, the original image may be divided into blocks and DWCT can be performed on each block instead of the whole image. Again we consider first the one-dimensional case. First, choose a block size M=2 The process of dividing an image into blocks is equally applicable to alternative transforms used at stage At the quantization stage
Let C(x,y) be, for example, the DWCT coefficients of an image block and Q(x,y) be the selected quantizer block. The quantization process is described by the following equation:
where [X] denotes the operation of rounding a number X into an integer. The values of the quantization coefficients Q(x,y) determine the precision with which the tranformed coefficients are stored, the highest precision being the precision of the data prior to transformation. Increasing the values chosen for Q(x,y), decreases the number of bits stored per coefficient. A convenient rule of thumb is that for a tranform of dimension N×N, a minimum practical value of Q(x,y) is N. If an un-normalized transform was used, the values of the first row of the quantization table, Q(0,y), can be chosen to compensate for the fact that the first row of transformed coefficients was not normalized. In some embodiments, a fixed number of quantizer tables are used in a given image such that a different coefficient block may use a different quantization table. For the coefficient blocks corresponding to the highest frequency coefficients, much larger quantizer values, for example, values on the order of 500, may be used. At the dequantization stage In the ordered statistics encoding stage Conventional methods of context prediction may be used at stage Coefficients of index 0 are predicted differently, utilizing the coefficients of index 0 in the neighboring blocks in addition to the same coefficients in the primary or second color components. A context of any order can be used; however, typically, orders 2 and 3 are used. An order 3 context is formed by three coefficients C The coefficients in a block are classified into 4 groups. The context for each group is formed using different rules: Group 0: Group 0 contains the coefficient at location (0,0) only. Group 1: Group 1 contains the coefficients on the first row except coefficient (0,0). This group of coefficients has an index represented by (0,i), with i>0. Group 2: Group 2 contains the coefficients on the first column except coefficient (0,0). This group of coefficients has an index represented by (j,0), with j>0. Group 3: Group 3 contains all the rest of the coefficients. This group of coefficients have an index represented by (i,j), with i>0, j>0. The context of a primary color coefficient P
If C
The locations of Groups 0, 1, 2, and 3 in a block of coefficients are illustrated schematically in FIG. The context for coefficient (0,0) is formed differently. First, all coefficients (0,0) from different blocks of the primary color are grouped together to form an index image. The index image is treated like a regular primary component block and DWCT is applied to the index image to obtain a coefficient block. The Groups 1, 2, and 3 are predicted using the context described above. The coefficient at position (0,0) is predicted using a constant 2 The context of a coefficient of a second color component S
If C
If C
The context for coefficient (0,0) of the second color component S is formed analogously to the context for coefficient (0,0) of the primary color component. First, all coefficients (0,0) from different blocks of the second color are grouped together to form an index image. This index image is treated like a regular second component block and DWCT is applied to this index image to obtain a coefficient block. The Groups 1, 2, and 3 are predicted using the context described above for the second component. Coefficient 0 is predicted using a constant 2 The context of a coefficient of the third color component Q
Similarly, the context of a coefficient Q
The context of a coefficient Q
The context for coefficient (0,0) of the third color component is formed analogously to the process described above for the (0,0) coefficients of the primary and second color components. In addition to the context from neighboring pixels, the positional index of each coefficient in a DWCT block can also be used as a context. The positional context is denoted by C Note that color components of an image in some quality scales, for example in scale 4, Bayer pattern subsampling, are not co-sited. In this case, a pixel of one color may not find corresponding pixels of other colors in the same location, leading to the effect that color blocks may not be aligned and may not even have the same block size. The block size problem can be handled by using only the subblock of the primary coefficients in the upper left corner for generating context for secondary colors. Alternatively, the misalignment problem can be overlooked. It may be further noted that the context prediction method described here may be applied in any data compression method. For example, the present context prediction method may be used in conjunction with entropy encoding steps in a JPEG- or JPEG2000-like method. The pixel context and positional context together form the whole context for probability modeling. The whole context is denoted by C At the ordering stage
Priority 0 is the region of highest priority and Priority 3 is the region of lowest priority. In the bitstream, regions of Priority 0 appears ahead of regions of Priority 1, followed by regions of Priority 2 and then 3. Each region is further partitioned into sub priority regions using the same method as shown above. This process continues until the region size reaches 1 pixel by 1 pixel. Table 19 shows the ordering result for a block of size 16×16. The same method can be applied to any sized block.
The output of the ordering method includes a table relating the two index pixel position to the coefficient order in the one-dimensional array as illustrated above. The final stage of the data compression method is entropy encoding The conditional probability P(C A value to be encoded is called a symbol. The prediction tables for different orders are disjoint, as they contain no common symbols. For example, if X is not in the prediction table of C The encoding stage completes the process of producing a bitstream that can be efficiently stored or transmitted over a computer network. The bitstream contains the encoded coefficients corresponding to the base quality scale image followed by the encoded coefficients corresponding to the differential images. In addition, the bitstream may contain conventional header information such as the size of the file, number of colors, number of scales, information about how the file is ordered, such as a block sequential index, identification of methods used, for example to transform and encode, non-default quantization tables, and optionally, probability tables. As described in FIG. 2, a decompression process essentially reverses the operation at each step of the process and thus enables the image to be reconstructed. The decompression process needs to have access to the probability tables used for entropy encoding. Although the probability tables may be stored as part of the header information, they are generally large. Therefore, at stage As displayed in FIG. 2, the recombination process However, it is not necessary to obtain a reconstructed image at the highest quality scale available in the compressed data. If a Display Quality Scale (DQS), the scale specified by an application, is of lower quality than the quality at which the image was stored, only the differentials corresponding to the DQS scale need to be recovered. In that case, alternatively, the flow of the processes in FIG. 2 can be altered so that the differentials G are obtained one at a time, from stages Thus, an image compressed according to the present process can be progressively viewed or downloaded by transmission over a computer network or the Internet. Further, a browser can display an image at a specified quality scale, ignoring any data corresponding to quality scales higher than the specified scale. Although the digital image compression process has been described with respect to specific scale definitions, transformations and ordering schemes, the description is only an example of the invention's application. Various adaptations and modifications of the processes disclosed are contemplated within the scope of the invention as defined by the following claims. Patent Citations
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